A Non-Parametric Test of Risk Aversion

Last registered on January 15, 2025

Pre-Trial

Trial Information

General Information

Title
A Non-Parametric Test of Risk Aversion
RCT ID
AEARCTR-0015143
Initial registration date
January 09, 2025

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
January 15, 2025, 5:21 AM EST

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Locations

Region

Primary Investigator

Affiliation

Other Primary Investigator(s)

PI Affiliation

Additional Trial Information

Status
In development
Start date
2025-01-22
End date
2025-01-27
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
In economics, risk aversion is typically modeled through a concave Bernoulli utility function within the expected utility paradigm. We propose a simple test of expected utility theory and risk aversion, based on mean-preserving spreads. Our findings will be contrasted with choices observed using the multiple-price list methodology.
External Link(s)

Registration Citation

Citation
Garcia Pola, Bernardo and Jacob Goeree. 2025. "A Non-Parametric Test of Risk Aversion." AEA RCT Registry. January 15. https://doi.org/10.1257/rct.15143-1.0
Experimental Details

Interventions

Intervention(s)
There will be 6–8 in-person laboratory sessions, involving individual decision-making tasks. Two treatment orders will be used:
1. First, multiple-price lists, followed by mean-preserving spreads.
2. Second, mean-preserving spreads, followed by multiple-price lists.

Intervention dates:
Between January 20th and January 27th, 2025, with specific dates depending on lab availability.
Intervention (Hidden)
Intervention Start Date
2025-01-22
Intervention End Date
2025-01-27

Primary Outcomes

Primary Outcomes (end points)
Subjects' choices in the mean-preserving spreads task.
Subjects' choices in the multiple-price lists task.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Demographic information, including gender (male, female or other) and age.
Time taken to respond.
Survey questions on risk preferences, including:
1. GSOEP + GSOEP about financial decisions (Dohmen et al. 2001; Charness et al. 2019).
2. Smoking, drinking, driving and obesity real life behavior questions by Barsky et al. (1997) and Anderson & Mellor (2008).
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The experiment involves subjects choosing between alternative lotteries. Sessions are divided into two parts: mean-preserving spreads and multiple-price lists. Instructions for each part will be provided immediately before the task. Subjects may ask questions during both the instructions and the experiment. At the end of the session, decisions from each part can be selected randomly for payment, and subjects will receive the corresponding cash, including a show-up fee.
The multiple-price list task follows the standard Holt and Laury (2002) format, where subjects make binary choices from a menu on screen, selecting the point at which they switch from one option to another.
The mean-preserving spreads lotteries are presented in several screens. There are several measures taken to reduce noise as much as possible:
- Every lottery comes with a visual representation for an easier comparison.
- The order in which lotteries are presented on the screen is randomized, as well as the position of the buttons for selecting one or the other.
- An "I don’t care" option is available, allowing subjects to express indifference, with which a lottery is selected for them.
- Mixed between the choices, subjects are presented with some “obvious” choices, such as a lottery that first order stochastically dominates another.
- Mixed between the choices, subjects are presented with repetition of choices taken previously, with lottery and buttons order in screens randomized as usual.
- Avoiding very small or large probabilities to prevent distortions: probabilities smaller than 1/10 or larger than 9/10, matching Holt-Laury limits.
Experimental Design Details
Randomization Method
Computer
Randomization Unit
experimental sessions
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
.
Sample size: planned number of observations
Sample size: 350-400 subjects, varying depending on laboratory attendance. This is much larger than the (132+62) subjects used by Levy and Levy (2001). It is is also justified by simulations, where a majority of choices are risk averse (>50%) and there is a 50% chance of simulated subjects choosing at random (noise). The simulation indicates that a sample of at least 230 subjects would be 99% confident that the low frequency of risk averse choices found in the pilot are not given by chance. Each treatment group has approximately half of the total subjects. Subjects are selected from the pool of the laboratory LEE of the Universitat Jaume I (Spain) with no specific criteria.
Sample size (or number of clusters) by treatment arms
.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan

Analysis Plan Documents

Pre registration.docx

MD5: f6c6106d61cb17fb230b7e7644e96121

SHA1: 9e129a10d9345cf72b0ed8e4d45a6c93fb21866e

Uploaded At: January 09, 2025

Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials